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Variance (Non) Causality in Multivariate GARCH

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  • Massimiliano Caporin

Abstract

This paper extends the current literature on the variance-causality topic providing the coefficient restrictions ensuring variance noncausality within multivariate GARCH models with in-mean effects. Furthermore, this paper presents a new multivariate model, the exponential causality GARCH. By the introduction of a multiplicative causality impact function, the variance causality effects becomes directly interpretable and can therefore be used to detect both the existence of causality and its direction; notably, the proposed model allows for increasing and decreasing variance effects. An empirical application evidences negative causality effects between returns and volume of an Italian stock market index future contract.

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  • Massimiliano Caporin, 2007. "Variance (Non) Causality in Multivariate GARCH," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 1-24.
    • Handle: RePEc:taf:emetrv:v:26:y:2007:i:1:p:1-24
    DOI: 10.1080/07474930600972178
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    References listed on IDEAS

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    Cited by:

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    2. Conrad, Christian & Karanasos, Menelaos, 2010. "Negative Volatility Spillovers In The Unrestricted Eccc-Garch Model," Econometric Theory, Cambridge University Press, vol. 26(3), pages 838-862, June.
    3. PIERRET, Diane, 2013. "The systemic risk of energy markets," LIDAM Discussion Papers CORE 2013018, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
    5. Yip, Iris W.H. & So, Mike K.P., 2009. "Simplified specifications of a multivariate generalized autoregressive conditional heteroscedasticity model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(2), pages 327-340.

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